eGain Corporation Aktie

Thank you for standing by, and welcome to eGain's Fiscal 2027 First Quarter Financial Results Conference Call. [Operator Instructions] I would now like to turn the conference over to Jim Byers, PondelWilkinson, Investor Relations. Please go ahead.

Thank you, operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2027 First Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy; and Chief Financial Officer, Eric Smit. Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements, which convey management's expectations, beliefs, plans and objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate or similar expressions.

Forward-looking statements are protected by safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects. Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, November 5, 2026, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call.

In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non-GAAP operating income. The tables included with eGain's earnings press release include reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures. eGain's earnings press release can be found by clicking News and Press within the Investor Relations page of eGain's website at egain.com. And along with the earnings release, we will post an updated investor presentation to the Investor Relations page. And lastly, a phone replay of this conference call will be available for 1 week. And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim, and good afternoon, everyone. I'll keep my comments on the quarter brief. Eric will take you through the numbers, but I do want to spend some time on our market because we believe the market and the category we've been building toward for a while is now taking shape. So our first quarter came in as we planned with revenue and profitability ahead of our guidance. Total revenue was $21.9 million, above the high end of our guidance. As expected, that was down 7% year-over-year, reflecting the planned runoff of our legacy business.

AI customer revenue was $14.4 million, up 7% year-over-year and represented 66% of total revenue. AI customer ARR was $54.5 million, up 11% year-over-year and grew to 75% (sic) [ 74% ] of total SaaS ARR from 72% at the end of fiscal 2026. Now I want to turn over to the category that I was talking about. In July, Gartner published its first ever Magic Quadrant for Customer Service Knowledge Management Systems and named eGain a leader. We were positioned highest in ability to execute and furthest in completeness of vision.

In the companion Critical Capabilities report, we received the highest product score for the compliance-driven service center use case. I want to underline why this matters beyond our position in it. Gartner does not initiate a Magic Quadrant on a whim. They do it when enough enterprises, their clients, are asking about a problem that becomes its own buying category. This is the category eGain has been building for a while. It now has a name, a boundary and a recognized leader.

But let's be candid, for many people, knowledge management, quote unquote, still conjures document libraries, intranet and maybe SharePoint on steroids. That is not what this category is about anymore, and it is not what eGain does. So let me explain what we do because it is key to the opportunity. Let me start by summarizing a little bit of what we discussed at our Solve event in Chicago last month, which is our annual customer event in the U.S. I talked about what I call the expert paradox. Simply put, AI works well for experts and struggles otherwise.

Let me explain. As you and we all know, AI is successfully used now in business for coding and personal productivity. In both cases, the user controls what goes into the AI system and the same user can judge the AI output quality. A developer, for example, can tell whether the AI-generated code works using tools and their expertise. Customer service use cases are at the opposite end of that spectrum in terms of user expertise.

So the person being given the AI answer, whether that is a service associate, a sales rep or the end customer is in no position to tell whether the answer is correct or compliant. And in the off chance that they do, they cannot control the input to AI anyway. This paradox largely explains the yawning gap in AI adoption for customer service adoption -- sorry, automation today. So how do we make AI work at scale for such use cases? We do 3 things together. We ensure that AI is fed trusted knowledge. We make sure it has effective guardrails, and we continuously check for output quality.

The policies, procedures and know-how a business feeds its AI systems are, in effect, the instructions that AI follows. Wrong knowledge means wrong AI results. That is what eGain provides, trusted knowledge and guidance for enterprise AI. Whether a business runs our AI agents, Salesforce's Agentforce, Microsoft Copilot or whatever they built themselves, we make sure every answer and every action performed by AI is grounded in knowledge the business can stand behind, kept current as products, policies and regulations change and continuously checked for quality.

At Solve, IDC presented new research sponsored by eGain. Based on a survey of 520 U.S. organizations with 1,000 or more employees, 3 findings stood out. 95% of organizations surveyed are already evaluating, piloting or running AI in their knowledge processes. Trying AI is universal, getting a return is not. The leaders report roughly twice the improvement of laggards on every business impact IDC measured. This is leaders and laggards as measured by their knowledge maturity. And the returns are measured in terms of AI ROI.

What separates them is the foundation. Half of the leaders run a fully operational unified knowledge platform versus 5% of laggards and 94% of leaders have automated more than half their knowledge processes versus 36% of laggards. IDC's conclusion is the one we have been making for some time. AI success does not hinge on the model. It hinges on the knowledge foundation beneath it.

The most powerful part of Solve for us was hearing our customers describe their AI ROI success in their own words. I'll mention 3. COUNTRY Financial, a Fortune 1000 insurer, has given more than 2,500 field agents trusted answers using the eGain platform instead of a list of documents to consult when they're sitting across the client or on the phone with them. Answer accuracy, they have seen is above 96%. Calls into their home office for escalation from their field agents has fallen by 25% in the first month and adoption has reached 70% with no mandates or incentives.

Their 2 lessons for the audience was, first, content beats model. And second, AI is not a project. It's a product you keep operating. Healthfirst, the second one I want to bring out, a New York-based health insurer, talked about publishing 1 governed update to 6,500 users in their business across 50 personalized portals. They feed that knowledge into call routing now, which cuts misrouted contacts by 76% (sic) [ 72% ], and that was the result they saw within 30 days. Knowledge is not only answering questions, it is driving decisions.

And the last one I want to bring out is Philip Morris International. They are now running 64 markets across multiple languages on 1 governed knowledge base on eGain. Their presenter put it best. Knowledge has left the customer care group, he said, it is now used across IT, marketing and field operations. This is a pattern we see again and again. We land in customer service where the bar for accuracy and compliance is high. Once the foundation is working, it becomes the knowledge layer for the rest of the enterprise.

The common thread in all these stories is that trusted AI is not a one-time project. Products launch, rates change, regulations update and the knowledge behind the AI starts getting stale pretty much the day you lock it. So at Solve, we introduced AI KnowledgeOps, a new framework along with a set of capabilities for running knowledge as a continuous operation. It runs as a loop. You start by learning what customers are actually asking from your customer conversations, you find the answer gaps, you prepare and organize the knowledge so AI can use it precisely, you test the answers and then you feed back what you learn from users into the loop.

We announced 5 new capabilities as part of this AI KnowledgeOps to make that loop fast and low effort. The first is a tool called WYCA, which stands for What Your Customers Ask. It analyzes customer conversations to show which questions matter most and runs inside the customer's own firewall. This is crucial for our compliance-oriented customer base. The second is kForge, which turns content scattered across silos and SharePoint, Confluence, ServiceNow, Salesforce, what have you, into AI-ready knowledge with experts approving anything that is high risk.

The third was knowledge graphs. We added that to our knowledge platform. It gives AI agents a map of how our business works, so they can reach and navigate to the right answer at much lower token cost and much faster. The fourth was Genie, which lets our users on the eGain knowledge platform ask the platform and the tools in a conversational interface to write or update content, run reports or build workflows. And finally, the last one and probably the most crucial to close the loop is Smart Evaluator, a product that allows AI engineers to continuously test AI pipelines for answer quality. It flags the wrong answer, it pinpoints where it likely went wrong in the pipeline and suggests a fix.

Here's why this matters commercially for us. For a major financial services company, our team ran the AI KnowledgeOps process 1 time, the first time in about 10 person weeks. That company had been expecting that, that whole exercise would have taken 1 to 2 years. Knowledge management has always been a great goal, but the effort it took to get knowledge ready and maintain it was the biggest barrier in this market, and we believe we have removed most of it.

A question which we ask ourselves and is fair is what stops the large platform vendors from doing it themselves? Well, 3 things. First is the depth of capability. The hard part is not search. It is governance, workflow, permissions, versioning, auditability for regulated businesses. We have solved that for many years, and this is where Gartner scored us highest. The second is we have deeply applied AI to our own platform to remove manual effort to make knowledge projects that typically took years now take weeks. We have automated knowledge management so businesses can operate this loop with ease and speed.

And third, we are open. We are modular. Enterprises will use many AI tools from many vendors. We sit underneath all of them. So customers can change AI models and agentic systems to drive their objectives without lock-in.

Turning to customers in the quarter. We signed several new customers and expansions during the quarter. Let me walk you through some examples because they'll show you where the market is going. The first one is Technology Credit Union. This is a growing multibillion-dollar credit union in the valley here. They recognize that their AI plans for members and employees depend on the knowledge underneath, and they're building that foundation across contact center, digital banking, branches and back office. So all employees, not just contact center agents.

They chose eGain over vertical-specific vendors for our governance, agentic capabilities and our connectivity into their core systems, in this case, Jack Henry and Alkami. Credit unions are now becoming a vertical for us that we are pushing in a dedicated way into. Alongside this new logo, we have customers like Rogue Credit Union, who presented at Solve, Oregon Community Credit Union and SELCO, among others, who are using eGain well beyond member service. And we are seeing that interest build in our pipeline.

The next new logo I want to talk about is a privately held multibillion-dollar consumer packaged goods company and their use cases outside customer service. They're deploying our AI agent to up to 1,000 plant floor operators, and this agent is going to be a virtual expert to assist them. Interestingly, this opportunity started with a self-sign-up on our website. This is a motion we've been building for some time, and it is starting to add opportunities to our funnel.

Another exciting new logo that we closed, but this one in early October, so not in Q1, is one of the oldest and largest investment management companies in America. They are deploying eGain as their trusted knowledge foundation to power all AI projects and thousands of employees. Our existing customers also continue to expand with more use cases and more functions. A large U.S. mutual insurer expanded the use of eGain alongside its investment in Salesforce using eGain Composer, our platform capability. They're making their governed eGain knowledge available inside their Salesforce AI. That is the role I described earlier. If the customer chooses a different AI application, eGain still provides the same consistent trusted knowledge underneath.

Another one I want to bring out is an international bank added our real-time knowledge access capabilities. So the trusted knowledge they have built to start with for customer service now flows automatically into many more of the bank systems and user groups. The pattern is consistent. Customers first establish the knowledge foundation and then AI application and ROI follows.

Looking at demand, the Gartner recognition is showing up at the top of our funnel. While it's early, I just want to bring out a few things. The Gartner Magic Quadrant campaign for marketing has quickly become one of our most productive digital programs, bringing in new conversations across North America and Europe. Our event-sourced leads have nearly doubled year-over-year this quarter as we have scaled marketing. We're seeing more RFPs from large enterprises, many of them inbound, which signals growing awareness.

In addition, we now have focused go-to-market teams, albeit small ones, targeting banks and credit unions, health care and government. And all this is complemented by our self-sign-up push and a growing partner network. To conclude my section, a year ago, very few people were talking about knowledge as a foundational requirement for AI. Today, Gartner has defined the Customer Service Knowledge Management category and named eGain a leader in it. IDC has quantified the AI payoff of getting the knowledge foundation right. And our customers are showing what AI ROI can be in customer service in production at scale.

Now our numbers do not yet reflect the size of that opportunity we understand, but we believe we are in the early innings of a large and durable market. We have the products, the proof points and the balance sheet to lead it, and we are now focused on effectively scaling our go-to-market execution. With that, I'll turn it over to Eric.

Thanks, Ashu, and thanks, everyone, for joining us today. As a reminder, we are using slides to support today's call. You can find them along with our updated presentation in the Investor Relations section of our website. As Ashu mentioned, we started the year well, delivering first quarter revenue and earnings above the high end of our guidance. I'll review our first quarter results, take you through our AI customer metrics, and touch upon our capital allocation and then discuss our outlook for the second quarter and full fiscal year.

Turning to our first quarter results. Starting with revenue. Total revenue was $21.9 million, down 7% year-over-year, but above the high end of our guidance and Street consensus. AI customer revenue grew 7% year-over-year to $14.4 million, above our guidance and represented 66% of total revenue. The year-over-year decline in total revenue reflects lower revenue from our legacy business, which declined approximately 28%, consistent with the transition we outlined last quarter.

Turning to gross margins. Non-GAAP total gross margin was 74% compared with 76% a year ago. Non-GAAP SaaS gross margin was 78% compared to 81% a year ago. The decline reflects the lower legacy SaaS revenue against a largely fixed hosting cost base.

Turning to operating expenses. Non-GAAP operating expenses were $13.6 million, up 4% year-over-year and down 4% sequentially. Sales and marketing expense were $4.6 million, up 17% year-over-year and down 16% sequentially, primarily reflecting the timing of program spending. Our largest event of the year, Solve26 in Chicago, that took place in October. So that spending falls in our second quarter. We expect sales and marketing spending to step up over the rest of the fiscal year as we expand the go-to-market programs Ashu described with a focus on converting growing market awareness into pipeline, new customers and recurring revenue.

Turning to the bottom line. GAAP net income was $1.6 million or $0.06 per share compared with $2.6 million (sic) [ $2.8 million ] or $0.10 per share a year ago. Non-GAAP net income was $2.4 million or $0.09 per share, exceeding our guidance and Street consensus compared to $4.7 million or $0.17 per share a year ago. Adjusted EBITDA was $2.7 million, a 12% margin compared with $5.0 million or a 21% margin a year ago.

Turning to cash flow and the balance sheet. Cash provided by operating activities was $1.8 million compared with $10.4 million a year ago. The difference is primarily due to the timing of an annual payment from a large federal customer. Last year, we received that payment in our first quarter. This year, it arrived early in the fourth quarter of fiscal 2026, contributing to the strong cash flow in that quarter. We ended the quarter with $71.3 million in cash and cash equivalents and no debt.

Moving to our capital allocation. Our capital allocation priorities are unchanged. First, invest in AI growth across product development and go-to-market to convert our category leadership into AI customer ARR. Second, maintain a strong debt-free balance sheet with significant liquidity relative to our scale. And third, return capital opportunistically. During the quarter, we repurchased approximately 644,000 shares for $3.6 million at an average price of $5.64 per share. Since the start of fiscal 2026, we have returned $15.2 million to shareholders through the repurchase of approximately 2.3 million shares, and we have approximately $6.0 million remaining under the authorization.

Now turning to our AI customer metrics. As we introduced last year, we report our business based on whether a customer is on our current AI-enabled platform rather than by product. AI capabilities, as Ashu described, are embedded across our AI-enabled platform, including identifying knowledge gaps, structuring content, authoring, verifying answers and powering AI. We, therefore, measure AI adoption at the customer level rather than by the individual product. Our focus is on growing the number of AI customers and expanding those relationships over time.

AI customer ARR includes all SaaS ARR from customers on our current AI-enabled platform and AI customer revenue includes SaaS and professional service revenue generated from those customers. With that context, AI customer ARR grew 11% year-over-year to $54.5 million as of September 30 and represented 74% of total SaaS ARR, up from 65% a year ago. Total SaaS ARR was $73.4 million, down 4% year-over-year, reflecting the continued declines among our legacy customers.

Turning to retention. Trailing 12-month dollar-based net retention for AI customers was 104%, consistent with the fourth quarter compared with 117% a year ago. As we noted last quarter, the year ago period included the significant expansion with the U.S. megabank that we signed in the fourth quarter of fiscal 2025. Net retention for all customers was 92% compared with 102% a year ago, reflecting the expected runoff of our legacy customers. Total remaining performance obligations were $71.0 million, down 18% year-over-year and short-term RPO was $52.0 million, down 11%. The decline reflects the timing of large multiyear renewals and the continued runoff of our legacy business.

Because RPO can swing significantly up or down with the timing of large multiyear renewals, we focus on AI customer ARR and net retention as a primary measure for our customer base. As AI customers become a larger share of our business, we expect their growth to increasingly drive our consolidated results.

Now turning to our outlook. Before the numbers, let me give you some context on how we are thinking about the rest of the year. First, investments. Part of the first quarter profitability outperformance reflected the timing of sales and marketing spending. We expect that spending to increase beginning in the second quarter, including the Solve26 event. As we expand the go-to-market programs, our focus is on converting increased market awareness into qualified pipeline, new customers and recurring revenue.

Second, the legacy transition. We expect the runoff of legacy revenue to become more pronounced beginning the second quarter, including the planned wind down of our legacy messaging business. Our full year outlook continues to assume an approximately 30% decline in our legacy business revenue. We remain comfortable with our full year outlook. Our balance sheet and operating cash generation gives us flexibility to fund our AI growth initiatives. Our full year guidance reflects a meaningful increase in sales and marketing investments over the remaining 3 quarters. As a result, first quarter profitability is not representative of the quarterly earnings levels we expect for the balance of the year.

So for the second quarter of fiscal 2027, we expect total revenue of $21.0 million to $21.5 million, AI customer revenue of $14.5 million to $14.8 million, GAAP net loss of $500,000 to net income of $200,000 or a loss of $0.02 to an income of $0.01 per share, including approximately $700,000 of stock-based compensation expense. Non-GAAP net income of $200,000 to $900,000 or $0.01 to $0.03 per share, adjusted EBITDA of breakeven to $500,000 or a margin of 0% to 2%.

For the full year ending June 30, 2027, we are reiterating our guidance. We expect total revenue of $84.5 million to $86.0 million, AI customer revenue of $59.5 million to $60.5 million, representing growth of approximately 8% to 10%. We continue to target approximately 20% year-over-year growth in AI customer ARR as of June 30, 2027. A GAAP net loss of $2.0 million to $3.0 million or $0.08 to $0.11 per share, including approximately $4.0 million of stock-based compensation expense, non-GAAP net income of $1.0 million to $2.0 million or $0.04 to $0.07 per share. Adjusted EBITDA of $650,000 to $1.4 million or a margin of 1% to 2%. We expect weighted average shares outstanding of approximately 25.8 million for the second quarter and 26.0 million for the full fiscal year.

Finally, a brief reminder of the long-term financial model we introduced last quarter. We see fiscal 2027 through fiscal 2029 as a transition period, with total revenue growth converging towards AI customer growth as the legacy business runs off. By fiscal 2030, we expect that transition to be largely complete. Our fiscal 2030 targets are AI customer ARR of $100 million to $120 million, up from approximately $54 million in fiscal 2026, a 17% to 20% compounded annual growth rate. Total SaaS ARR of $100 million to $120 million with AI customers representing substantially all of it, up from 72% in fiscal 2026.

AI customer revenue of $105 million to $115 million, up from approximately $55 million in fiscal 2026. Total revenue of $110 million to $120 million with AI customer revenue approximately 95% of the total, up from 60% in fiscal 2026. SaaS gross margins of approximately 80% with positive adjusted EBITDA margins as we invest in growth.

The result we are building toward is a larger, faster-growing AI knowledge infrastructure company with a greater share of recurring AI customer revenue and significant expansion opportunity within our installed base. We have shown we can run this business profitably at our current scale. We are choosing to trade some near-term profitability for growth because we believe the size of this opportunity warrants it.

So to summarize, we delivered first quarter results ahead of our guidance, and we are reiterating our full year outlook. AI needs the right instructions, and eGain is the instruction layer for enterprise AI, recognized as a leader in the first-ever Gartner Magic Quadrant for our market. AI customers now account for 74% of our SaaS ARR on a path to substantially all of it by fiscal 2030. And with $71.3 million in cash and no debt, we have the resources to fund the plan and turn category leadership into durable long-term growth. With that, I'd like to open the call for questions. Operator?

[Operator Instructions]

And your first question comes from the line of Jeff Van Rhee with Craig-Hallum.

This is Daniel on for Jeff. Maybe, Ashu, if you would want to touch a little bit on the AI product suite, obviously, has been expanding a lot. There's a whole suite of things that have been rolled out and every Solve event, there's a lot more that gets announced. Maybe whether it's AI KnowledgeOps or AI agents, too, there's a lot of products that have been announced. Maybe you could talk to what you expect to be the biggest needle movers in the AI portfolio and maybe what products or product developments you would want investors to be focused on?

Good question. So the way we think of it is the foundation first, and that's the AI Knowledge Hub. That is where it all starts. And then all these tools and products that go around the platform then get added on top. But what we have done with the AI KnowledgeOps as a framework is now to guide more and more people, what we have heard from customers, including at Solve was you have so many things to offer. We would love for you to tell us exactly how it all works together and how to go about it from where we are today to where we want to be.

So it all starts with AI Knowledge Hub and then gets -- so if you think of it as layers, it's sort of AI Knowledge Hub as the core platform. And then on top of it, you have tools and you have these prebuilt AI agents, right? So in the target market we are going after, which is the enterprise, I think AI Knowledge Hub with a platform is where it all starts.

Okay. That's helpful. And then maybe just on the S&M investments you're planning to make. Just walk through a little bit more of the plan there, what you're thinking to change in terms of is that about certain leaders? Is that about more quota-carrying reps we're targeting a particular geography? Just how you're thinking about what those plans entail?

Right now, we -- as I mentioned, so we've scaled up our front-end marketing investments to drive more awareness and more footfall, if you will, into our digital and physical presence, right? So that's where we are most focused on in terms of increasing our spend on sales and marketing.

And then the second bit behind that is building out the capacity to engage with those early leads and turn them into opportunities. So building out the SDR team, if you will, to make sure that we have got the right level of expertise and front-end engagement for those who we are able to bring in as leads. So I think those are the top 2 things for us right now. The back end of the funnel, which is more around selling and closing, we think that we have enough capacity for that. So it's really about filling the top of the funnel.

And then just 1 modeling question, Eric. In terms of the messaging business exit, I know the first half of that was achieved in 2Q '26. So it took half impact to Q2 and then another half of the impact came in Q3. And then the remaining half of the messaging business exit, I know that was -- that happened here in Q1. Just in terms of thinking whether we got a full quarter of impact or not, was that exited at the beginning of the quarter, near the end? How much of a quarter of impact did we get from that messaging exit?

So that was the full quarter. It finished up at the end of the quarter. So that's why we mentioned that we expect to see that decline in Q2.

And your next question comes from the line of Eric Suppiger with B. Riley Securities.

This is Ethan Widell on for Eric. To start off with, so AI customer revenue up 7% versus 11% for ARR. Can you maybe help bridge the gap there between those 2 numbers and maybe what we should expect going forward?

Yes. So I think, obviously, like you would typically see with the ARR is always the leading indicator as you grow business, then as those contracts get signed, that would take time for that to convert into the revenue going forward. So I think for us, as we -- as you look to the guidance, I think we're targeting a continued improvement in the ARR growth rate, targeting that by the end of the year, we'd be up to 20% whereas currently, our guidance for the revenue is sort of roughly half of that, and that's more just a lag between the ARR booking and then the revenue that gets recognized.

Okay. That makes sense. And then so when we look at the kind of legacy ARR decline, of those customers rolling off, maybe can you speak to what share of that is customers churning out versus migrating to like the AI platform? And has the Gartner recognition changed kind of what that looks like?

It's a good question. We are working on it. But right now, it's not like we have seen a significant shift there.

Understood. Well, it seems like a very powerful validation of your technology. So congrats on that, and that's all for me.

[Operator Instructions]

And I'm showing no further questions at this time. I would like to hand it back to the management for closing remarks.

Great. Well, thanks, everyone, for taking the time today. Look forward to continue updating you after our Q2 results. Thanks. And I guess 1 final item, I'll be at the ROTH investor event later this month. So it would be great if anybody in the New York area that's participating. Great to meet up with you. Thank you.

Thank you. And ladies and gentlemen, this concludes today's conference call. Thank you all for joining. You may now disconnect.

Good day, and welcome to the eGain Fiscal 2026 Fourth Quarter and Full Year Financial Results Call.

[Operator Instructions]

Please note this event is being recorded. I would now like to turn the conference over to Jim Byers, Investor Relations. Please go ahead.

Thank you, operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2026 Fourth Quarter and Full Year Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy; and Chief Financial Officer, Eric Smit.

Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements, which convey management's expectations, beliefs, plans and objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate or similar expressions. Forward-looking statements are protected by safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects.

Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, September 3, 2026, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call. In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non-GAAP operating income. The tables included with the earnings press release include reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures.

eGain's earnings press release can be found by clicking the Press Releases link on the Investor Relations page of eGain's website at egain.com. And along with the earnings release, we will post an updated investor presentation to the Investor Relations page. And lastly, a phone replay of this conference call will be available for 1 week.

And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim. Good afternoon, everyone. Right at the end of fiscal 2026, the category we've been building toward for years got a name. In July this year, Gartner published its first ever Magic Quadrant for customer service knowledge management systems and named eGain a leader, positioned highest for ability to execute and furthest for completeness of vision. This inaugural Magic Quadrant matters more than just our position in it. This is the first time a top analyst firm has drawn a sharp boundary around this market and explicitly called out knowledge management for customer service as its own category of enterprise infrastructure.

Now they base it on the volume and kind of client inquiries they get in this area. And therefore, they have chosen to invest Magic Quadrant level resources and attention to it. It's a very important signal for the market and the category that's building around it. As we have said, there's good reason this buying category is emerging now. Generative AI has collapsed the old separation between instruction and data. What an AI agent or agentic workflow does in any live customer or employee assistance conversation is determined entirely by the policies, procedures and know-how it is fed.

When that knowledge is wrong, the AI is confidently wrong. When it's stale, the AI doesn't know it's out of date. So knowledge is no more documentation just for humans to optionally use. It is instruction for AI. Wrong knowledge equals wrong AI. Engineering that instruction layer, governing it, operating it continuously is what we call AI Knowledge ops, a term that Gartner reflected in their Magic Quadrant report as something unique and important that eGain brings to this solution. It is the discipline enterprises are now realizing they cannot skip if they want AI to reliably work in production, not just in pilot.

With this market trend and the analyst acknowledgment, let me walk through how fiscal 2026 came together. Before I do that, let me define a term that we will use moving forward, and that is AI customer. An AI customer is an eGain customer who utilizes one or more of our AI offerings. So with that said, let's look at full year fiscal 2026. Our total revenue grew 3% to $91.1 million. Our AI customer revenue grew 20% year-over-year. AI customer ARR grew 13%, and represented 72% of total SaaS ARR at year-end, up from 63% at the midpoint of fiscal 2026. This is an intentional shift in the shape of our customer base. A growing majority of our SaaS ARR now sits with customers who are using one or more of our AI capabilities.

Turning to new business. Our momentum continued to build. In the fourth quarter, we won several new logos. A couple of examples here. First, a leading European insurance company, they set out to automate their service operation with AI and recognized that they needed to put in place a governed knowledge foundation before they could deploy AI automation at scale. So they selected eGain to modernize their knowledge environment and establish that foundation.

Second, a global multi-energy operator serving millions of customers. They faced a familiar barrier to scaling service, fragmented knowledge leading to inconsistent service quality. They are deploying our knowledge platform and AI agent in one contact center. Based on the successful blueprint from that deployment, they will extend to the rest of their contact centers. They also plan to activate self-service channels and leverage the Knowledge Hub across the entire business.

In addition, we added several new paid pilots this quarter. Increasingly, we see buyers wanting to extensively validate our platform in their own environment before committing to a full rollout, and they're willing to pay for it. This is a shift from where we used to be where we were doing a lot of free quick trials and pilots as part of our innovation in 30 days, the 30-day and no-risk pilot that we have.

Converting these paid pilots into at-scale production rollouts is a focus for us this fiscal year. Give you a couple of examples again. One of the world's largest pharmaceutical companies. Their use case is that their experienced scientists and specialists retire or change roles in their R&D teams and the company risks losing a lot of deep tacit expertise. They're using our AI Knowledge Hub to capture that tacit knowledge on a continuous basis and turn it into valuable knowledge for their AI engine.

Second, a global leader in testing inspection and certification. They were facing a hard regulatory deadline, and they needed accurate instant guidance in a compliance-heavy environment. Early pilot results of our deployment indicate that the AI agents deliver 95% self-service resolution, and it's enjoying a strong 80% customer user satisfaction surveys.

Third, a global leader in gaming technology. They operate in a complex environment where every answer has to be guided and correct. Stepping back to the market, I want to share 2 trends that we see emerging in the last couple of quarters. First, businesses are treating knowledge as core AI infrastructure, and their tech and AI teams are actively building on top of this infrastructure, which drives demand for richer platform capabilities like real-time knowledge APIs and stringent service levels. So our growing developer-facing capabilities on our Composer platform are being well received.

Second trend we see is growing interest in customer self-service projects. Several new logos in the recent quarters have started out with self-service deployments, something we did not see a year ago when it was more common to start with contact center-based use cases. While contact center productivity is still of great interest, we sense that businesses are increasingly driving for ROI at scale on their AI investments.

Moving to business momentum in fiscal 2026. Our new logo wins increased 27% year-over-year. As I mentioned earlier, several of the new logos we acquired in fiscal '26 have paid pilots in Global 2000 accounts, and they have significant upside, something we intend to pursue this fiscal year. Our pipeline opportunities valued at $500,000 ARR or more, doubled in count year-over-year. And our core verticals, which are compliance heavy like banking, financial services, insurance and health care, we grew our opportunities in the pipeline by 40% year-over-year, exactly where a trusted knowledge foundation matters the most.

Turning to products. Our innovation continues to accelerate with focus. Everything we launched in last quarter, which is in Q4 during our London eGain Solve event in May, fueled the cycle of knowledge and AI. First, we're increasingly deploying AI in our platform to dramatically automate knowledge management. And the result of that generation and maintenance of trusted knowledge with low effort then drives better instruction to AI that is being used to reliably automate customer service and customer operations.

So a few of the noteworthy announcements of new capabilities we made in May. The first was the eGain IVA, which is an intelligent voice agent. What's unique about it is that it is using the same trusted knowledge platform as we use for all our digital self-service tools. So that consistency and quality is something that now we can offer as a complete omnichannel self-service offering.

Secondly, our eGain Agentic Studio, which is a zero-code application building environment we have launched so that business users can assemble these service use cases end-to-end, multistep complex processes with every step grounded in verified knowledge using assured tools and actions and invoking human oversight when needed. It's a complete platform for service automation using agentic capabilities.

The third, which we had announced in the past is the eGain Evaluator, which is our continuous evaluation tool for AI pipelines, but that is -- we made it generally available, and it's a capability that's getting a lot of interest from our large customers who are looking to drive continuous quality assurance of their agentic pipelines.

And finally, we announced a new vertical for health care, which is our eGain AI Knowledge Suite for health care. And this is a governed knowledge foundation purpose-built for health plan and health systems. We will build on this momentum at our upcoming Solve event in Chicago on October 13 and 14 this year. We'll lay out our view of the year ahead, the shift from knowledge management to knowledge automation and the value of agentic AI assembly on top of trusted knowledge. And of course, we'll announce new capabilities and hear from our customers and partners.

So in conclusion, our sustained bet on AI knowledge, the market and products in fiscal 2026 is showing results. And so we are doubling down, and we intend to lead this market. With that, I'll turn it over to Eric Smit, our CFO, to take you through the financial details. Eric?

Thanks, Ashu, and thanks, everyone, for joining us today. Before I begin, I'd like to note that we are again using slides to support today's call. We believe this provides helpful context and makes it easier to follow our results and outlook. You can access the slides in the Investor Relations section of our website alongside the webcast.

As Ashu noted, fiscal 2026 demonstrated solid financial execution. Total revenue increased 3% to $91.1 million. AI customer revenue grew 20%. Adjusted EBITDA increased to $13.6 million and cash provided by operating activities reached a record $21.2 million.

I'll review our fourth quarter and full year results, explain the transition in more detail to our customer-based AI metrics and discuss our fiscal 2027 outlook and long-term financial framework.

Starting with the fourth quarter results and starting with revenue. Total revenue was $22.2 million, exceeding both our guidance and Street consensus compared with $23.2 million in the prior year quarter. The year-over-year decline in total revenue primarily reflected the lower revenue from our legacy conversation and analytics customers. AI customer revenue grew 11% year-over-year in the fourth quarter. Looking at gross margins, non-GAAP total gross margin for the quarter was 72% compared to 73% a year ago. Non-GAAP SaaS gross margins were 78% compared to 80% a year ago.

Turning to operating expenses. Non-GAAP operating costs were $14.1 million, up 6% year-over-year and 2% sequentially. Sales and marketing expenses were $5.5 million, up 21% sequentially, reflecting our planned investments in go-to-market initiatives, including the eGain Solve event that we held in London.

Looking at our bottom line, GAAP net income was $1.3 million or $0.05 per basic and diluted share compared with GAAP net income of $30.9 million or $1.13 per basic share and $1.11 per diluted share in the prior year quarter. The prior year results included an approximately $29 million tax benefit from the release of the majority of our valuation allowance. Non-GAAP net income was $2.1 million or $0.08 per share on a basic and diluted basis, exceeding our guidance and Street consensus. This compares with $2.4 million or $0.09 per share on a basic and diluted basis in the year ago quarter.

Adjusted EBITDA was $2.2 million, representing a 10% margin and exceeding our expectations compared to $4.5 million and a 19% margin a year ago. During the quarter, we repurchased 1.4 million shares for $10.1 million at an average price of $7.32 per share.

Turning to our full year results. Looking at our revenue, total revenue was $91.1 million, exceeding our guidance and up 3% year-over-year. Within total revenue, AI customer revenue grew 20% year-over-year. AI customer ARR grew 13% year-over-year and represented 72% of total SaaS ARR at year-end. Looking at gross margins and operating expenses. Non-GAAP total gross margin was 74%, up from 71% in fiscal 2025. Non-GAAP operating costs were $55.3 million compared to $56 million in the prior year.

Turning to the bottom line, balance sheet and cash flows. GAAP net income was $8.9 million or $0.33 per basic share and $0.32 per diluted share compared with $32.3 million or $1.15 per basic share and $1.13 per diluted share in fiscal 2025. As I mentioned, the prior year results included approximately $29 million tax benefit. Non-GAAP net income was $13 million or $0.48 per share on a basic basis and $0.47 per share on a diluted basis, up from non-GAAP net income of $5.7 million or $0.20 per share on a basic and diluted basis in the prior fiscal year.

Adjusted EBITDA increased to $13.6 million, representing a 15% margin, up from $8.6 million and a 10% margin in fiscal 2025. Cash flow from operations reached a record $21.2 million, representing a 23% operating cash flow margin, up from $5.3 million or a 6% operating cash flow margin in fiscal 2025. Cash and cash equivalents totaled $73.3 million at June 30, 2026, compared to $62.9 million at June 30, 2025. During fiscal 2026, we repurchased 1.6 million shares for $11.5 million at an average price of $7.16 per share. At year-end, we had $9.7 million remaining available under the $60 million buyback authorization.

Now turning to our AI customer metrics. As Ashu mentioned, instead of reporting by product hub, going forward, we're now reporting based on whether a customer is actively using one or more of our AI offerings. We call this AI customer ARR and AI customer revenue. And we believe it's a cleaner, more forward-looking way to show our AI adoption spreading across our installed base since many customers now use AI capabilities across multiple parts of our platform rather than within a single hub. This is the framework we'll use going forward.

The strategic rationale is straightforward. We have found that the customers' overall adoption of our AI capabilities, not the specific product SKU or hub they originally purchased is the strongest predictor of long-term retention expansion. To better measure and ultimately maximize that dynamic, we completed a full review of our customer base this year and segmented it into 2 groups: AI customers, meaning those actively engaged with our AI platform and all other customers. This is a meaningful shift in how we think about the business.

Our reporting focus is now on growing ARR per account, which we view as a primary measure of success with a specific mix of products or given customer consumes becomes secondary. We believe this customer base view better reflects how customers deploy our integrated platform, how we manage these relationships and the broader retention and expansion opportunity within our AI customer base. It is now our primary lens for measuring the health of our AI business.

AI customer ARR is defined as total SaaS ARR from customers who are actively utilizing one or more of our AI offerings. This amount includes all offerings associated with the customer and not solely the AI offerings. AI customer revenue is defined as the total revenue generated from customers who actively utilize one or more of our AI offerings, inclusive of their SaaS and professional services revenue. This amount also includes all offerings associated with the customer and not solely the AI offerings.

With that context, here are the metrics. AI customer ARR increased 13% year-over-year and represented 72% of total SaaS ARR at year-end. Total SaaS ARR declined 1% year-over-year, driven by the decline among our legacy non-AI customers.

Turning to our retention rates. Trailing 12-month dollar-based net retention for AI customers was 104% compared to 120% a year ago. As a reminder, we had closed a significant expansion deal with JPMC in Q4 of last fiscal year, which drove that increase in net retention. Net retention for all customers was 93% compared to 105% a year ago. Total remaining performance obligation or RPO of $87 million was down 5% year-over-year and short-term RPO of $62 million was down 2% year-over-year.

Now turning to our outlook. Starting with guidance for the first quarter of fiscal 2027. We expect AI customer revenue of between $13.7 million to $14 million and total revenue of between $20.9 million and $21.4 million.

Turning to the bottom line. For Q1, we expect GAAP net income of $500,000 to $1 million or $0.02 to $0.04 per share, which includes stock-based compensation expense of approximately $900,000. We expect non-GAAP net income of $1.4 million to $2 million or $0.05 to $0.08 per share and adjusted EBITDA of $1.4 million to $1.9 million or a margin of 7% to 9%. For the fiscal year ending June 30, 2027, we expect AI customer revenue of between $59.5 million to $60.5 million, representing growth approximately of 8% to 10%. Total revenue to be between $84.5 million and $86 million.

Our outlook reflects 2 different trends within the business. We expect continued growth from AI customers alongside an estimated 20% decline in revenue from our profitable legacy customers. We are using the cash generation from this non-core business to fund investments in the larger AI opportunity. We expect ARR from AI customers to grow approximately 20% in fiscal 2027, while ARR from legacy customers is expected to decline by 60%.

On the bottom line, we expect GAAP net loss of $2 million to $3 million or $0.08 to $0.11 per share. This includes stock-based comp expense of approximately $4 million, non-GAAP net income of $1 million to $2 million or $0.04 to $0.07 per share and adjusted EBITDA of $650,000 to $1.4 million or a margin of 1% to 2%. We expect weighted average shares outstanding of approximately 26.6 million for the first quarter and 26.8 million for the full fiscal 2027.

Today, we are also introducing a long-term financial model that lays out our targets through fiscal 2030. As we complete our transition to a higher-growth AI-led business, we see fiscal '27 through fiscal '29 as a transition period with total revenue growing both increasingly converging with AI customer revenue growth and fiscal 2030 is a year that convergence is largely complete.

Now turning to our long-term financial model. For fiscal 2030 relative to fiscal 2026, we are targeting AI customer ARR of between $100 million to $120 million, up from $54 million in fiscal 2026, a 17% to 22% CAGR as AI ARR compounds towards scale. Total SaaS ARR of $100 million to $120 million, up from $75 million in fiscal 2026, reflecting substantially complete runoff of non-AI ARR and migration to AI.

For AI customer ARR, we expect that's going to represent approximately 100% of total SaaS ARR, up from 72% in fiscal 2026, effectively a pure-play AI ARR base with increasing contribution from our AI business. AI customer revenue of $105 million to $115 million, representing a 17% to 20% CAGR from the $55 million we generated in fiscal 2026 and a 20% plus growth year-over-year by fiscal 2030, making our underlying AI revenue growth increasingly visible in our total results. And total revenue of $110 million to $120 million, representing approximately 15% to 20% growth year-over-year by fiscal 2030, with total company growth now closely mirroring our AI growth.

AI customer revenue representing approximately 95% of total revenue, up from 60% in 2026, supporting a higher quality valuation framework and SaaS gross margins of approximately 80%, maintaining our attractive software margin profile and adjusted EBITDA margin that remains positive while we fund AI growth, a deliberate balance between growth investments and profitability discipline.

We believe our leadership in AI-powered knowledge management, expanding market opportunity and increased go-to-market investment position eGain to pursue durable growth while maintaining an attractive profitability profile.

So to summarize, in closing, AI customer revenue and ARR both grew at double-digit rates in fiscal 2026, and we completed the shift to a customer level reporting that we believe gives investors a clearer view of the business and strengthens our positioning following Gartner's naming of eGain a leader in the inaugural Magic Quadrant for Customer Service Knowledge Management Systems. We also delivered total revenue growth, strong profitability and record operating cash flow in fiscal 2026.

With our strong balance sheet and cash generation, including the cash we generated from our declining but profitable legacy offerings, we are all in on the AI knowledge opportunity, investing to build on that position and pursue sustainable long-term growth.

Lastly, as Ashu mentioned, we will be hosting an Investor Day and Analyst Day in conjunction with our upcoming eGain Solve customer event on October 13 in Chicago. Additional information and registration details are available on our website. This event is a great opportunity for prospective investors and analysts to meet with customers and learn more about our business. We hope you can join us.

With that, I would like to open the call for questions. Operator?

[Operator Instructions]

Our first question comes from Jeff Van Rhee with Craig-Hallum.

This is Vijay on for Jeff. First one for me, just in the target model and kind of here in the prepared remarks, you talked a little bit about running off the non-AI ARR. Is there a time line for that in mind kind of similar to what you had with the messaging business? And then just how does the profitability of those businesses compare to the rest of the business?

Thanks for that. Yes. So for clarification, if you -- as we sort of described in the model, the expectation is the non-AI business should be substantially -- the goal, obviously, is to convert some of that into the AI business. But from a modeling standpoint, we'd expect that to be to 0 as we get to the 2030 time frame.

Got it. And then you talked a little bit on previous earnings calls about some of the potential impacts of AI more generally on the business, maybe pricing pressure on SaaS products. Are you seeing that show up in the business at all? Or is that still kind of expected later down the line?

This is Ashu here. So I would say that we are seeing some pressure of that, but we are also seeing our ability to create new product offerings, which layer on kind of additional revenue from these value-added AI capabilities. So all in all, the effect has not been as significant as I would have feared. Yet, I mean, we are prepared for it. We do think that there may be -- my sense is 1 or 2 points pressure over the next 2 to 3 years is how I see it. But Eric, do you have anything more to add?

Exactly. Yes, I think that's sort of aligned at this stage. I think given the construction layer that this is building, it's sort of creating opportunities that are different from what we would have seen historically as well, which I think will obviously impact sort of the way the pricing we approach this.

Yes. Got it. And then just for the target model, obviously, I appreciate having that out there. As you look at the growth profile, is there any way you can segment that as far as if you expect 15% or 20% growth, how much of that will be maybe price or new customer adds or adding seats to existing customers or reducing churn? Just what do you think the biggest kind of drivers there will be?

So I think most of the driver will come from new logo acquisition. I think when we look at the opportunity in front of us, especially with now the backdrop that we're seeing with the Gartner MQ, I think this investment to drive the brand awareness and scale up the customer base will be the primary driver. Obviously, we will work hard to move customers that are in the legacy bucket, but that will not be the primary driver for this growth.

[Operator Instructions]

Our next question comes from Erik Suppiger with B. Riley.

This is [ Ethan White ] calling on for Erik. Just one question from me. As companies adopt an ecosystem of AI models rather than just using one of the frontier models, does that dynamic create more demand for a knowledge management solution? Can you maybe speak to that dynamic a little more?

Yes, I'll take that, Eric. Yes, you're right. What we are seeing now is in the last month or so, I'm sure you've seen as well, a lot of talk about people running into sort of token runaway costs and also just cost of AI as the adoption has been pushed hard in enterprises. And what we see with our approach to it is just by being sharper in what you are feeding into these AI tools, you can keep the costs down significantly, sometimes by a factor of 10.

So it's a big advantage by being more precise in how you instruct and guide rather than throwing the kitchen sink of content and context into these models. So that's one thing we see as a very interesting advantage that we bring to the party.

The second one is that we even internally inside the platform tend to be smart about using, if you will, horses for courses, the right models for the right need. And we see that as another way of managing the AI token cost for our clients.

And maybe just one little follow-up. Does that dynamic matter at all in kind of the big frontier models versus open source? Or is that relevant?

It does matter to some extent, the quality advantage, as you know, in terms of benchmarks and stuff is probably not more than 10% to 15% for most of the relevant benchmarks that we are looking at. And the cost difference can be more than a factor of 10. So yes, it does matter. And what we see is as businesses are doing more and more real-time continuous operation to ensure that their knowledge and know-how is always up to date, and that's going to drive up token usage, and that will then require smarter routing to the relevant capable models.

[Operator Instructions]

At this time, there are no further questions. I would like to turn the conference back over to eGain management for any closing remarks.

Thanks, operator, and thanks, everyone, for joining the call today. And again, I encourage all of you out there to look at joining us at the event in Chicago, again, details on the website. Thank you.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.

Good day, and welcome to the eGain Fiscal 2026 Third Quarter Financial Results Call. [Operator Instructions] Please note, this event is being recorded.

I would now like to turn the conference over to Jim Byers, PondelWilkinson, Investor Relations. Please go ahead.

Thank you, operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2026 Third Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy; and Chief Financial Officer, Eric Smit.

Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements, which convey management's expectations, beliefs, plans and objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate or similar expressions. Forward-looking statements are protected by safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects.

Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, May 14, 2026, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call.

In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non-GAAP operating income. The tables included with the earnings press release include a reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures. eGain's earnings press release can be found by clicking the Press Releases link on the Investor Relations page of eGain's website at egain.com. And along with the earnings release, we will post an updated investor presentation to the Investor Relations page of eGain's website. And lastly, a phone replay of this conference call will be available for 1 week.

And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim, and good afternoon, everyone. Thanks for joining us. We delivered a strong third quarter with continued momentum in our AI Knowledge business, driven by customer expansion, growing partner engagement and new products. Revenue was in line with expectations and profitability remains strong. Year-to-date, our AI Knowledge ARR has grown 26%, and we have generated $18.7 million in operating cash flow year-to-date, which is a 27% margin.

Let me share some of the interesting highlights that we are seeing in the business. In the last 60 days, we've seen a meaningful increase in RFP activity in the U.S., most of it from Fortune 1000 BFSI, which is banking and insurance and health care enterprises. These RFPs almost always seem to focus on AI readiness of knowledge, an open architecture for APIs and MCPs and deep integration into the customer service, customer experience stack.

Equally importantly, many of these RFPs are coming through our partners. Year-to-date, our partner-sourced opportunities are up 67%. We see the growing interest in AI knowledge as a natural progression from an early adopter phase to an early majority phase of the adoption curve. Knowledge management, we see is being increasingly seen as a core AI infrastructure, a must-have, not a nice-to-have.

Switching to customers. Now, we had a very nice quarter for product adoption and expansion. These expansions reflect a pattern of customers standardizing on eGain as their enterprise knowledge platform.

Let me highlight some examples. The first one is a top 10 U.S. insurance company. This client expanded from an initial deployment of about 3,000 licenses in one business unit to an additional 5,600 licenses in a second major business unit. This creates a single knowledge hub across these divisions, replacing the fragmented and siloed content and knowledge they had before.

With this platform, the client is now establishing consistent taxonomy, knowledge workflows and content life cycle governance across these business units, while analytics and AI help them continuously refine knowledge. This client is also piloting AI Agent, which is one of our products for the contact center, powered by the trusted knowledge coming from our platform.

The second example I want to share is a top 10 global airline. To support growth in their customer care department, the client has added licenses to ensure consistent knowledge access across all the new teams, reinforcing eGain as the single platform for knowledge-powered service and operational efficiencies.

We're also seeing rapid follow-on expansion from newer clients. I'd give you a couple of examples. After selecting eGain to support a large-scale digital transformation a few months ago, this European financial services conglomerate is now expanding usage across other business units beyond customer service in the contact center to look at self-service options for all their touch points.

Another example is a global engineering services leader. They initially deployed our solution for field service knowledge, and now they're expanding to assist all their service personnel, including contact centers and partners. Across these examples, there is a theme, and that is that once we are deployed in a CX or customer service use case, the eGain platform naturally expands to become the centralized enterprise knowledge platform, both for AI and humans.

Looking at products during the quarter, we introduced several innovations to deliver greater value in some of our strong verticals and also deepen our ecosystem integrations.

First, we launched the eGain AI Knowledge Suite for retail banking. The solution is purpose-built for banks and credit unions to unify knowledge and enable AI-driven service and needs-based guided selling. Early clients like Rogue Credit Union are very excited about the positive user adoption and accelerated time to value, something they shared during a joint webinar last month.

Then we introduced our AI Agent for Cisco Webex Contact Center, strengthening our proposition in the Cisco ecosystem.

Third, we announced connectors into UCaaS platforms, Microsoft Teams, Slack and Zoom Team Chat, all with the goal to enhance employee collaboration with the same trusted knowledge. These connectors will help our clients build knowledge once for CX use cases and then reuse it for employee-facing use cases across their business.

And it's, again, a pattern that we are seeing emerging where we land into the CX world, which is customer service or contact center. And once we show our solution and deploy the success of that then drives a natural extension of that knowledge platform across the rest of the use cases, which are more employee-facing.

Finally, we announced enterprise AI connectors to agentic development environments, including Copilot, Claude, Gemini and Cursor. These connectors enable developers to tap into trusted knowledge managed within the eGain platform via APIs and MCP protocols right from their favorite development environment. As I said before, this idea of a trusted governed knowledge base and a hub is very compelling. It connects and controls all the AI projects, including prototypes and offers governance, explainability, observability to developers and business users alike in the business.

As I zoom out of the customers and specific products that we announced last quarter, all of us would agree that the pace of innovation is accelerating in the market, and so it is with eGain. We see lots of opportunity to increasingly automate the capture, curation and consumption of knowledge, that loop as it relates to customer service and contact centers in regulated businesses and companies with complex products.

Last week, we hosted our annual Solve 26 event in London. We have another annual event in Chicago in October, but this one is a European event in London for customers and partners. The event reinforced what we are seeing across the market, trusted knowledge is becoming the essential foundation for enterprise AI. The reason is simple. Conventional wisdom says that knowledge is nothing more than unstructured data, not true. Knowledge is the instruction layer for AI. It provides the what, and the how and occasionally the why that is used by the models to then deliver automated experiences that are reliable.

To build these agentic systems, enterprises must first centralize, govern and improve this knowledge. So the quality of knowledge determines the quality of AI outcomes. This is especially important in customer service and contact centers, which represents one of the largest near-term opportunities for AI transformation. At the same time, our research shows that more than 80% of organizations are still in the very early stages of their AI knowledge maturity and transformation journey, and that creates a significant opportunity for eGain.

At our Solve event, we also launched several new products beyond the ones we announced last quarter. And these help our clients consume the knowledge more easily in agentic workflows. They enable our clients to evaluate and ensure quality of these AI Knowledge pipelines they're building all the way from content to begin with and automated experiences that the AI tools deliver.

We also launched an IVA product, which brings accurate conversational self-service to the voice channel. And finally, we announced an AI agent for Salesforce version 2, which is a pluggable solution that activates our AI Agent with full context of Salesforce content and data within the Salesforce Service Cloud desktop.

Customers and partners love the new capabilities and what they appreciated the most was their fellow customers sharing their knowledge journey and AI ROI stories. Customers like Achmea, BT, BMI, Specialized Bikes, Worldpay, they shared their insights, including tips and tricks, very, very valuable for attendees. And for us, it is gratifying and inspiring.

On the team front, during the quarter, we strengthened our leadership team with the appointment of Steve Pappas as Head of Innovation. Steve brings deep expertise in knowledge management, AI and customer experience, along with a strong track record of scaling enterprise SaaS businesses and a sharp focus on helping clients modernize their knowledge architecture. His leadership will help us deliver more consumable innovation and accelerate market expansion as we continue to shape the AI Knowledge category.

To conclude, we delivered strong financial performance, expanded within customers and are building a high-quality pipeline driven by growing enterprise demand for AI-powered knowledge. As the market increasingly recognizes Trusted Knowledge as the foundation for enterprise AI, we are well positioned to lead this category.

With that, I'll hand it over to Eric.

Thanks, Ashu, and thanks, everyone, for joining us today. Before I begin, I'd like to note that we are again using slides to support today's call. We believe this provides helpful context and makes it easier to follow our results and outlook. You can access the slides in the Investor Relations section of our website alongside the webcast. As Ashu noted, we delivered a solid third quarter with year-over-year growth in both revenue and ARR, along with continued strong profitability.

Let me walk you through our Q3 financial results, followed by our outlook. Looking at our revenue. Total revenue for the third quarter was $22.5 million, up 7% year-over-year. SaaS revenue also grew 7% year-over-year and represented 93% of total revenue. Excluding the approximately $600,000 quarterly impact from noncore messaging products we are sunsetting, total revenue and SaaS revenue would have been up 13% and 14%, respectively, year-over-year. Revenue was also impacted by approximately $450,000 due to the 2 fewer days this quarter compared to the prior quarter.

Looking at non-GAAP gross profits and gross margins. Total gross margin for the quarter was 74%, up 500 basis points from 69% a year ago. SaaS gross margin was 78%, up 100 basis points year-over-year. This expansion was driven by continued improvements in SaaS margins and a greater mix shift of higher-margin SaaS revenue relative to professional services revenue.

Now turning to our operations. Non-GAAP operating costs for the third quarter was $13.9 million, up 1% year-over-year and down 3% sequentially, reflecting ongoing discipline as we streamline operations and benefit from automation and our shift towards a product-led sales model.

R&D was up 3% sequentially, reflecting continued investment in engineering talent and leadership. We expect the trend towards approximately 30% of revenue over time as we invest to support innovation and growth. Sales and marketing expense was $4.5 million for the quarter, down 11% sequentially, though we expect this to increase in Q4 as we invest in go-to-market initiatives, including our recently completed eGain Solve event in London.

Looking at our bottom line. Non-GAAP net income was $3.2 million or $0.12 per share on a basic basis and $0.11 per share on a diluted basis, up significantly from $765,000 or $0.03 per share on a basic basis and diluted basis in the year ago quarter. Adjusted EBITDA margin was 14% at the high end of our guidance range and up from 6% a year ago.

Turning to our balance sheet and cash flows. We used $1.8 million of cash in the third quarter, reflecting typical seasonality and collections, which are weighted toward the first half of the fiscal year. For the first 9 months, cash flow from operations was $18.7 million, representing a 27% cash flow margin, well ahead of our expectations. We end the quarter with $80.5 million in cash, up from $62.9 million as of June 30, 2025, and we have no debt, maintaining a strong balance sheet and financial flexibility.

Now turning to our customer metrics. To highlight the strength of our Knowledge business, we are breaking out our ARR metrics for Knowledge customers. SaaS ARR for Knowledge customers increased 26% year-over-year and SaaS ARR for all customers increased 7% year-over-year. Excluding noncore messaging products, SaaS ARR growth for all customers increased -- would have increased 11% year-over-year.

During Q3 2026, 1 on-premise subscription customer in EMEA chose not to migrate to our product suite in the eGain Cloud and as a result, terminated the agreement with us. This reduced our total SaaS ARR impact of approximately $1.6 million. And of that, the AI knowledge component of their business was approximately $900,000. We view this as a one-off event given the restrictions in the customers' country of origin on the use of cloud-based services. Also, as expected, bookings reflected normal seasonal trends with Q3 typically being the softer quarter based on historical patterns.

Our retention rates also improved significantly. LTM dollar-based SaaS net retention for Knowledge customers was 116%, up from 97% a year ago, while net retention for all customers was 101%, up from 88% a year ago. LTM dollar-based SaaS net expansion rate was 120% for our Knowledge customers and 107% for all customers. Looking at our remaining performance obligations, total RPO increased 11% year-over-year, and our short-term RPO of $48.5 million was up 9% year-over-year. These metrics reflect strong engagement and expansion, particularly within our AI Knowledge offering.

Before turning to our guidance, I'd like to share some additional color on the factors influencing our updated FY '26 revenue estimates. As Ashu stated, we are seeing a clear shift in the market. AI Knowledge is now being evaluated as enterprise infrastructure rather than solely as a contact center solution. This aligns directly with how we're positioning the platform and is creating larger, more strategic opportunities that we believe we are well positioned to win.

That said, these larger opportunities typically involve longer sales cycles, which are affecting the timing of revenue conversion. But, to the guidance, for the fourth quarter of fiscal 2026, we expect total revenue of between $21.5 million to $22 million.

Turning to the bottom line. For Q4, we expect GAAP net loss of $300,000 to net income of $400,000 or $0.01 negative to $0.01 positive per share, which includes stock-based compensation expense of approximately $900,000. We expect non-GAAP net income of $600,000 to $1.3 million or $0.02 to $0.05 per share and adjusted EBITDA of $500,000 to $1 million or a range of 2% to 5%.

For the full fiscal year ending June 30, 2026, we expect total revenue to be between $90.5 million to $91 million, representing a return to growth for the year. GAAP net income of $7 million to $7.8 million or $0.25 to $0.28 per share. This includes stock-based compensation expense of approximately $2.9 million. It also includes warrant expense of approximately $1.4 million, and non-GAAP net income of $11.3 million to $12.1 million or $0.39 to $0.42 per share. and adjusted EBITDA margin of $11.9 million to $12.4 million or a margin of 13%. We expect weighted average shares outstanding of approximately 28 million for both the fourth quarter and the full fiscal 2026.

In conclusion, we delivered a solid quarter with revenue and ARR growth and strong profitability. Our AI Knowledge Hub ARR grew 26%, highlighting continued momentum. We are executing well against our go-to-market strategy. While it's still early, we are seeing encouraging signs, including increased high-quality RFP activity and pilot programs. We remain focused on expanding our market reach and building on our leadership position in AI knowledge.

With that, I'll turn it back to operator for Q&A.

[Operator Instructions] The first question will come from Jeff Van Rhee with Craig-Hallum.

Just a few for me. On Ashu, I guess this would be for both of you. On the RFP surge and the increase in activity, can you just put a little more scoping around that in terms of the magnitude of late-stage opportunities at this point maybe versus 6 months, 12 months, 18 months ago? Just I don't know, put some context around that increase in RFP activity that you referenced.

Sure. I would say that the number of RFPs that we are actively -- that we have responded to, right, in the last 60 days is probably about double of what our average rate in 60 days would be, right? So that's one lens to look at.

In terms of the stage of decision around those RFPs and the eventual conclusion into wrap up, I'd say that's a 2- to 4-month process, I would assume for most of them, right? So those are the 2 comments I would make.

Got it. And then maybe I know you're not giving formal guidance for '27, but can you put some bounds around how you think about the year based on what you've got in ARR, what you're looking at there in pipeline, even if it's broad ranges, do you see positive top line? Is there -- are there scenarios where you think double-digit top line is credible? I don't know, anything you would offer there would be helpful.

So a couple of thoughts there. One, I don't know if I have the numbers right away to give you numbers, but I would say that the number of new logos is going to go up substantially, [ perhaps, ] in the sort of the target profile that we are going after, right? So that I'm feeling pretty optimistic about.

And the other thing I would say is that the expansion in existing accounts is picking steam, and that's something we saw even in the last quarter. And so that to me bodes well in terms of average ARR per customer. So put those 2 together, I feel like our AI Knowledge ARR should definitely grow double digits in the '27 time frame.

Eric, do you have anything to add?

Exactly. Yes. I think, as we see the AI Knowledge piece now 64% of total business, and that will -- we expect that to continue to increase. And certainly, that component certainly we would expect that to continue to grow in double-digit numbers.

Helpful. Maybe just one last question. The -- I guess, there is a 2-part. Just maybe any update on the Cisco relationship. And then obviously, you're building cash. You got a pretty healthy cash balance at this point. Just thoughts on use of cash, returns of capital, how you're thinking about that?

I'll take the first one. Maybe you can take the second one, Eric.

Okay.

So my -- so the Cisco relationship is active and healthy. I think there is more that we can do. And so we are working on seeing how we can partner with them more, especially as some of the AI Agent capability that we have, as you noted, we've announced last quarter in their Webex Contact Center platform. So yes, that's an area that I think is an opportunity for us to further expand our engagement with them in their ecosystem.

And then I think on the use of cash, I mean, obviously, in this environment, having a very healthy balance sheet, we feel very comfortable in this position, both in our focus on the go-to-market execution. So obviously, continuing to be careful in that investment, but recognizing how dynamic and exciting this opportunity is, we want to make sure that we continue to invest in sort of the position and the go-to-market.

Obviously, there are seasonal aspects of when the money gets spent. So Q3 is historically a slower spend for us. That's why the numbers were down, but that's spend -- as I indicated, we are spending more in Q4, especially with the big customer events. So that's typically what you would see.

And then certainly, we will be opportunistic when it comes to other options, especially in this environment, if there's a plan to acquire customers through inorganic means, we're always open to that. But our primary focus here is driving execution on the core business operation. And we do have $20 million -- roughly $20 million available in our buyback program. So again, depending upon where the stock price is, we would certainly look to sort of reengage on the buyback that we paused for the last quarter or so.

The next question will come from Erik Suppiger with B. Riley.

Two questions. One, the RFP activity, why do you think that's increasing? Do you think that is a function of just market awareness for the need for better knowledge management? Or is that more a function of some of the outreach that you've had?

And then secondly, Salesforce announced that it's expanding into the Agentforce Contact Center. It sounds like they're going to be really pushing an integration between CRM and contact centers going forward. Do you think that changes market dynamics in terms of your opportunity going forward as CRM starts getting more blended or the vendors doing CRM get more blended with contact centers?

Right. So the first question about why I think the RFP activity, I would say -- I'd like to say that it has to do entirely with our marketing outreach, but I think it has, as much to do with the market awareness. And awareness around the fact that these AI investments are not scaling and not scaling in ROI positive ways. So that's the theme we are seeing even in our conversations with prospects who are not in our pipeline. They're all struggling with having made big bets on things like Copilot across the enterprise or a few of them working with Gemini, Google or OpenAI.

The theme we hear is consistent and that is the foundation is not right. And so it's like a whack-a-mole constantly trying to figure out what part of it broke down in terms of the inputs into the AI system. I think that is as much to -- as a contribution factor as our marketing efforts.

In terms of your second question, I would say, at this point, I haven't seen that impact any conversations that we are in. And we -- let's say, the most popular CRM system in our target customers is Salesforce. So we do see a lot of Salesforce. We are used to -- we integrate with them, we enhance these -- we work with their content, all that. So we have not seen too many examples of people saying, "Oh, I'm going to throw away my XYZ CCaaS and just go with Salesforce as the entire solution for CRM plus CCaaS yet.

Do you think it would be more difficult or easier for you to get into an account that has an integrated CRM and contact center solution?

It's a hypothetical. I -- we haven't seen any of those, but I would say that Salesforce generally has an open ecosystem architecture. And so we have not seen that being a huge challenge if the clients decide they want to explore a best-in-class solution like ours for their knowledge layer in their AI kind of strategy.

[Operator Instructions] Showing no further questions, this will conclude our question-and-answer session. I would like to hand the conference back over to management for any closing remarks.

Right. Thanks, operator, and thanks, everyone, for joining us today. Look forward to updating you once we finish out the year and give updated to our plans for FY '27. Thank you.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.

Good day, and welcome to the eGain Fiscal 2026 Second Quarter Financial Results Conference Call. [Operator Instructions] Please note, this event is being recorded.

I would now like to turn the conference over to Jim Byers with PondelWilkinson Investor Relations. Please go ahead.

Thank you, operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2026 Second Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy; and Chief Financial Officer, Eric Smit.

Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements which convey management's expectations, beliefs, plans, and objectives regarding future financial and operational performance.

Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate, or similar expressions. Forward-looking statements are protected by safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects.

Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, February 3, 2026, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call.

In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non-GAAP operating income. The financial tables included with the earnings press release include reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures.

eGain's earnings press release can be found by clicking the Press Releases link on the Investor Relations page of the eGain website at egain.com. And along with the earnings release, we will post an updated investor presentation to the Investor Relations page of eGain's website. And lastly, a phone replay of this conference call will be available for 1 week.

And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim, and good afternoon, everyone. We saw good business momentum in our second quarter. Both revenue and profitability exceeded our guidance and street consensus, and we delivered strong operating cash flow. We also drove strong bookings in the quarter, including multiple Global 1000 logos and healthy expansions.

Our AI Knowledge Hub momentum continues to grow with ARR from these customers up 27% year-over-year and our total AI Knowledge ARR now representing 64% of our total SaaS ARR. Finally, we saw more than 50% year-over-year increase in top-of-the-funnel AI Knowledge leads. So that's encouraging.

Turning to business highlights. We saw a couple of trends worth calling out. First, 25% of our new logos in the first half of fiscal '26 were sourced by partners. This is more than a doubling of our partner-sourced new logos year-over-year. Our partner momentum is building nicely. We also saw enterprise buying bundled alongside the CX deals in the majority of cases in this quarter.

This convergence of customer service and contact center buying and enterprise use case-oriented buying is accelerated by what we see as corporate AI teams and their interest in knowledge. This is a development that validates our point of view that a centralized trusted knowledge foundation is necessary for AI ROI upscale.

Now let me share some booking highlights for the quarter, starting with new logos. First, we won the enterprise knowledge mandate for one of the largest business software providers in the world. Our knowledge platform will be deployed across over 100,000 users and multiple use cases across CX, employee experience, and AI experience, which now is, as we know, a huge thing. This win also presents us the opportunity to partner with this client to potentially offer our knowledge solution to their global clients, very exciting for us.

Another one worth calling out is a large U.S.-based manufacturer of kitchen cabinets with over 15 brands and 6,000 employees. This company's product and policy knowledge was scattered all over, and it was making it very hard for their service teams to access accurate current answers for long-tail products. As you know, cabinets last for a long time and service questions can be for products you have sold 15 years ago. They selected our AI knowledge hub to centralize all the knowledge, drive consistency, and automate their future AI initiatives.

We also signed a couple of new insurance logos during the quarter, including Achmea. This is one of Europe's largest insurance and financial services groups based in Netherlands. They serve more than 10 million customers. Achmea has been driving a strategic shift towards becoming a digital insurer with customer experience and self-service adoption as their core priorities.

They recognize the need for a knowledge as a service partner, and they selected us to accelerate their digital insurance journey. Our platform now will power 21,000 users across Achmea, including customer service and contact center use cases, but not just customer service.

We are also seeing good momentum with credit unions. One of them that I want to call out is Oregon Community Credit Union. They serve over 250,000 members in Oregon, Idaho, and Washington. They recognized the need to modernize their knowledge system, and they selected us, again, for our open architecture and AI capability. Yet again, in this case, eGain will be used to support all enterprise use cases, including contact center use cases where we are integrated and connected into the Genesys CCaaS platform.

So this convergence that we see of starting with customer service and contact center, but then the knowledge platform being used for all enterprise use cases is a welcome one for us because we believe that while CX is the most compelling ROI proposition for knowledge, knowledge is not limited in terms of its value within customer experience use cases alone. And so applying what works in CX across the entire enterprise provides enormous benefit to businesses that are looking to accelerate their AI ambition.

In terms of thought leadership, we more than ever believe that the market is aligning with our view that a trusted knowledge foundation accelerates enterprise AI ROI. This quarter, as I mentioned earlier, we saw more than 50% year-over-year increase in top-of-the-funnel marketing leads and a 23% increase in pure inbound interest year-over-year.

Turning to partnerships, which is a key area for us beyond our direct go-to-market motions. Our expanding efforts in partner development are bearing fruit. Partner-sourced leads in the first half of fiscal '26 increased 80% year-over-year. On the product front, as we have mentioned before, we announced our developer-focused offering, the eGain Composer in October at our customer event in Chicago.

eGain Composer now is helping us drive more product sales of the AI Knowledge Hub and attract new ecosystem partners. We are seeing growing engagement from developers, both from AI groups within enterprises as well as smaller partners who are building bespoke AI solutions for their clients, which are enterprises. As you know, Composer offers modular capabilities on our composable platform to easily build trusted solutions using our Knowledge Hub.

While it's early days, we see continued and growing interest from smaller partners as well who want to use Composer to build differentiated capabilities in their own go-to-market offerings. As we execute more go-to-market programs around Composer, we intend to expand awareness amongst AI stakeholders, increase engagement, and generate early conversations across diverse use cases beyond customer experience and customer service.

I'm also excited about the fact that eGain was once again noted in the right top quadrant, the leader quadrant in the Gartner Emerging MQ for generative AI knowledge apps. We were also late last year named KM World's Readers' Choice Award winner, and this came back in November of 2025.

To conclude, we're executing well on our go-to-market strategies. We are seeing growing brand awareness as evidenced by increase in inbound interest in our products. We are seeing increased opportunities both in our go-to-market awareness and direct marketing activities as well as partner activities. We are seeing our product-led growth strategy delivering tangible results.

And lastly, we see the market converging centered around CX and customer service, but extending beyond that to cover the entire enterprise for a centralized knowledge foundation that businesses are looking to create, which will then give them the right capability to springboard off for their AI initiatives.

With that, I'll hand it over to Eric Smit, our CFO, to provide more detail on the financials. Eric?

Thanks, Ashu, and thanks, everyone, for joining us today. Before I begin, I want to mention that we are again using slides to support our earnings call. We believe this will provide helpful context and make it easier for you to follow our results and outlook. In addition to the webcast, you can find the slides in the Investor Relations section of our website, under the updated investor presentation.

As Ashu noted, we had a strong -- had strong business momentum in the quarter with revenue and profitability exceeding our guidance and street consensus, strong year-over-year ARR growth, expanding gross and EBITDA margins, and strong cash flow from operations. Let me share more details about our Q2 financial results before discussing our outlook and guidance for Q3 and fiscal 2026.

Looking at our revenue, total revenue for the second quarter was $23 million, ahead of our guidance and street consensus and up 3% year-over-year. SaaS revenue increased by 5% year-over-year and accounted for 95% of total revenue, up from 93% in Q2 last year. If we exclude the approximate $600,000 reduction per quarter from our noncore messaging products, which we are sunsetting this fiscal year, then total revenue was up 5% year-over-year and SaaS revenue was up 8% year-over-year.

Looking at our non-GAAP gross profits and gross margins. Total gross margin for the quarter was 74%, up 300 basis points from 71% a year ago. SaaS gross margin for the quarter was 80%, up 200 basis points from 78% a year ago. The SaaS gross margin expansion was primarily driven by our product enhancements, which enabled more cost-efficient deployments and delivered operational efficiencies within our cloud and customer support teams.

PS revenue was sequentially lower in Q2 as anticipated due to the timing of bookings that didn't close until late in the quarter as well as the impact of the government shutdown. This contributed to the negative PS margin in Q2. We have rightsized and adjusted our PS organization during Q2, and we'll see the full quarter savings benefits in Q3 onwards. As such, we expect PS gross margins to return to flat to slightly positive as we saw in Q1.

Now turning to our operations. Non-GAAP operating costs for the second quarter were $14.2 million, down 3% year-over-year as we have streamlined and realigned our business operations, increasing our investments in AI product innovation while reducing spend on legacy products.

Looking at our bottom line. Non-GAAP net income was $3 million or $0.11 per share on a basic and diluted basis, up from $1.3 million or $0.05 per share on a basic basis and $0.04 per share on a diluted basis in the year ago quarter. Adjusted EBITDA margin for the quarter was 14%, up from 7% in the year ago quarter.

Turning to our balance sheet and cash flows. For the second quarter, we generated strong operating cash flow of $10.1 million, representing a 44% operating cash flow margin compared to $6.4 million and 29% operating cash flow margin in the year ago quarter. Our cash collections have historically been front-loaded in the fiscal year due to the timing of large deals and renewals.

Our balance sheet remains very strong and with a healthy level of cash and no debt. Total cash and cash equivalents at the end of the quarter were $83.1 million, up from $62.9 million as of June 30, 2025. During the company -- during the quarter, the company did not repurchase any shares of common stock. And as of the end of Q2, we still have $19.7 million remaining available under the company's current authorized buyback program.

Now turning to our customer metrics. I've broken out our AI Knowledge ARR metrics from the total metrics to highlight the momentum of our AI Knowledge business. SaaS ARR for AI Knowledge customers increased 27% year-over-year, while SaaS ARR for all of our customers increased 7% year-over-year. Excluding the noncore messaging products, which we are sunsetting this fiscal year, SaaS ARR for all customers increased 11% year-over-year.

Turning to our net retention rates. LTM dollar-based SaaS net retention for AI Knowledge customers was 116%, up from 99% a year ago, while net retention for all customers was 101%, up from 89% a year ago. Our LTM dollar-based SaaS net expansion rate was 119% for our AI Knowledge customers and 108% for all customers.

Looking at our remaining performance obligations, total RPO increased 15% year-over-year, and our short-term RPO of $53 million was up 4% year-over-year.

Now turning to our guidance. For the third quarter of fiscal 2026, we expect total revenue of between $22.2 million to $22.7 million, and as a reminder, the fewer number of days in Q3 has an approximately $400,000 negative impact on revenue for the quarter when compared to Q2.

Turning to the bottom line for Q3. We expect GAAP net income of $1 million to $1.5 million or $0.04 to $0.05 per share, which includes stock-based compensation expense of approximately $800,000. We expect non-GAAP net income of $1.8 million to $2.3 million or $0.06 to $0.08 per share and adjusted EBITDA of $2.6 million to $3.1 million or a margin of 12% to 14%.

Looking at our full year ending June 30, 2026, we expect total revenue to be between $90.5 million and $92 million, representing a return to growth for the year. This remains unchanged from our initial guidance provided last quarter.

We now expect GAAP net income of $4.5 million to $6 million or $0.16 to $0.21 per share. This includes stock-based compensation expense of approximately $2.9 million, also includes warrant expense of approximately $1.4 million. Our non-GAAP net income of $8.8 million to $10.3 million or $0.31 to $0.36 per share and adjusted EBITDA of $10.9 million to $12.4 million or a margin of 12% to 13%.

Looking at weighted average shares outstanding, we now expect approximately 28.3 million shares for Q3 and 28 million for fiscal 2026.

So in conclusion, we delivered revenue and profitability that exceeded our guidance. Our AI Knowledge ARR growth continues to gain momentum, increasing 27% this quarter and now accounts for 64% of total SaaS ARR. We're executing well on our go-to-market strategies and seeing positive results, and we are well positioned to capture market leadership in AI-driven knowledge automation and to drive sustainable revenue growth and increased profitability going forward.

Lastly, on the Investor Relations calendar, tomorrow, we will be hosting virtual meetings with institutional investors throughout the day as part of the Oppenheimer Emerging Growth Conference. We hope to see some of you virtually. And next month, we will be at the Annual ROTH Conference on March 23. We'll provide more details as we get closer to the date and hope to see some of you there in person.

This concludes our prepared remarks. Operator, we will now open the call for questions.

[Operator Instructions] The first question comes from Jeff Van Rhee with Craig-Hallum.

A couple of questions for me. First, congrats on the large software deal, and I'd love to hear a bit more about that deal, the cycle, the competitive landscape, what you're replacing?

Sure. This is Ashu here, Jeff. Yes. So let's go in order. So the first thing was it was a fairly long sales cycle. We've been at it for, I would say, seriously for about 1.5 years, but we've been talking to them maybe a few months before that, went through the whole RFP process. And eventually they selected us. So that was one.

The second was in terms of what we were replacing, they didn't have an enterprise-wide knowledge platform up until then, but they did have other competitors for AI search in some of their functional groups, but they didn't have an enterprise-wide knowledge solution up until then.

Got it. And then, Eric, just on the numbers front, just so I'm thinking about this right, the March quarter versus the December quarter is a clean sequential compare. We've had the full run here of the large JPMorgan deal. And then if I recall, the messaging was going to sunset in tranches. And I think you had said that was Q1 '27. Just trying to validate if I've got all that right.

Yes, that's correct. The -- just to be clear, on the noncore messaging, we had 50% of it reduced in the Q2 quarter and then the balance will be reducing in the first quarter of '27.

Yes. Okay. All right. Got it. And then on the partner side, I mean, obviously, having some real good impact. Can you just narrow that down? Is that concentrated within a small basket? Are there any particular partners that are driving the strength on the partner-driven lead gen?

Yes. The ones that seem to be working, 2 areas that seem to be quite promising. One is small boutique kind of knowledge consulting shops, which have existing clients, and they are looking to kind of refresh the platform for the clients and some of the legacy knowledge vendors who these boutique SI shops have worked with. So that's one area where we are seeing good momentum.

And the second area we're seeing some early but very promising momentum is just pure contact center knowledge deals through sort of the TSD kind of networks.

And congrats on the cash flow, guys. Great cash flow there.

The next question comes from Richard Baldry with ROTH Capital.

Sort of an esoteric question, but there seems to be a stable developing that people can use AI and sort of instantly create software companies. So could you talk a little bit about the challenges to replicating a system of record class software platform that a vibe coding session clearly couldn't replicate. So you've been building this for a decade plus. I'd like to just put a pin in that myth, sort of one company at a time. So if you can talk about sort of the barriers to and the moats around what you've built, I think that would be helpful.

Yes. That's a tough one, Richard, but I'll try to take a crack at it because the fact is that you're right, basic programming and even complex programming, given appropriate constraints and very good prompting and kind of co-coding, if you will, with very good programmers -- human programmers on the other side is accelerating the phase of software creation, which we typically call coding and development, right? So that part is definitely true and we are taking advantage of it just as anyone else is.

The parts that I think are still seem more work, though I wouldn't ever say that it's not solvable because the trajectory seems to be quite interesting, is architecture, understanding of use cases, understanding things that are nonfunctional in nature in terms of reliability, scalability, performance, and so on. And then putting it all together. What I'm trying to say is I think the barriers are definitely coming down for everyone, right, whether it's a 1-person shop or whether it's a 500-person shop like eGain or it's a 300,000-person shop like Google. And the focus and the use case understanding and the ability to put things together that have proven track record are going to be the differentiators really.

And then think about internally then, how much do you think you can use the tools internally to either speed up your own future development and/or lower your cost to deliver the service? And maybe a broader way to think about it is in a pre-generative AI world, do you think your profitability would be higher? Or is it higher in a post-world because you can lower some of your internal costs?

I think assuming that pricing holds, we would definitely be more profitable, right? But I do suspect that there's going to be pressure on pricing over time for all of us, not just eGain. Everyone is going to feel the pricing pressure. And so I feel like there is a big opportunity to become the enterprise knowledge fabric. Yes, we're starting from the vantage point of customer service and customer experience. But as I mentioned, a big majority of our deals now are starting in CX. And in the first purchase itself, they end up including enterprise use case.

So I think that it's a land grab really at this point. And so we are focused on this knowledge piece and we hope to grab more land than others and then play the game as well as anyone else from there on.

Got it. And maybe last for me. The cash pile start building up pretty fast. Any thoughts on the best way to efficiently deploy that in the current environment?

No. I think as we've said before, I think we will keep focusing on our internal investments to drive the top line growth. I think we'll continue to -- on that momentum, I think we're obviously benefiting from the AI innovations that are sort of helping us save some of those money even as we're spending it on R&D and ramping up the teams in Sunnyvale. I think some of those cost savings are resulting in those improved profits in the short term.

But I think we certainly continue to see that as a primary focus. We obviously still have the share buyback in place. So we'll see whether that's an appropriate vehicle. And then finally, opportunistically looking at inorganic options. Again, this is not something that's a primary focus. But of course, we will continue to evaluate those as they come in as we look forward.

And maybe one last one for me. How are you thinking about hiring plans? You're sort of midway through the year executing against the plan. When you look forward, whether that's pipeline-driven, opportunity-driven, do you think hiring will be concentrated in sales and marketing? Do you think there's a flat period for some like development while you adopt the new tools that are getting you higher throughputs there? Just how do we think about that as you head into the second half?

So we've been, as Eric mentioned, Rich, we've been investing pretty smartly and actively in hiring new talent in product over the last year, right, in locally in the Bay Area. And that has proven -- that's borne fruit. We're very happy with that. Now at the same time, we have been reducing from some of the other sort of distributed teams that we had, thanks to automation that we are driving in the business. So the net impact doesn't seem like much is happening, but there's a lot of reallocation going into much more high-end engineering and technology talent on the product side, both in product management as well as engineering and architecture and AI.

Then moving to marketing, we -- as you know, we brought in a new marketing head. He's brought in a couple of new people now. And I think marketing is going to see a lot of increased activity and investment for us in the second half of this fiscal year, right? And then finally, sales, which while we are very happy with the pipeline, I still think that given the way we are product-led now, our sales motions are kind of different. They're much more expert-led, much more specialist-led, especially in large organizations where there's very early contact with tech and AI folks.

And so the composer proposition is resonating very well, and we are investing in that group, right? So that's kind of where we are now as it scales, which I believe it will, then we will add more sort of systematic sales muscle and headcount to meet the demand.

The next question comes from Erik Suppiger with B. Riley Securities.

Congrats on a real solid quarter there. One, I just want to be clear, is the deployment at JPMorgan, is that fully rolled out? Or is that still in process?

And then two, you had talked about some additional areas for opportunities across the enterprise. Can you discuss what are some of the logical kind of low-hanging fruit once you move past CX?

Sure. So first on the JPMC question, we are not fully rolled out as planned. We are halfway there and we expect to be fully rolled out later this year as planned. So that's good.

The second question in terms of other use cases, we are currently working on a few, Erik. I think maybe this is a good time for me to mention that we pulled up our Europe-based customer event, which normally we would do in June. Now we're doing it in May this year. And so we'll have our eGain Solve customer event in London on May 6 and 7. And those are the 2 events, one in Chicago, one in London, where we bring out our new products and capabilities.

And so I would say we look forward to sharing more in that forum as to what are the other use cases we are looking to roll out. But you can logically think of the other use cases. But to me, the first expansion is going to be concentric around broadly customer operations as opposed to just customer service and from there on into other enterprise-facing areas.

Okay. And then just in terms of some of the wins that you had, are most of those greenfield wins? Or are you displacing anybody's solutions?

I would say that in more cases than not, we are replacing some tactical solution or some big solution that has just run out of gas in terms of new innovation or AI readiness. That's one of the things we are seeing a lot of is existing AM solutions in CX areas that just don't seem to be investing in modernizing and making it sort of AI ready, if you will. And our knowledge automation capabilities are -- and composer capability. These 2 things are super attractive to businesses that are looking to drive sort of their AI projects with trusted content feeding into those systems. That seems to be one of the big trigger points.

Are customers using CRM platforms for that and they're finding that they're insufficient? Or what is it that they're trying to use for that?

I say there are 3 things, right? One is they just have a whole bunch of SharePoint and they have tried to rag kind of retrieval augmented generation on top of lots of SharePoint repositories to try to deliver the right content to AI systems on the front end. And they just run out of gas trying to make it work. That's one. I'm giving you some archetypes here.

So the second is they have knowledge sitting in salesforce, and they just run out of patience waiting for salesforce to deliver to what the commitments have been. Let's just say that.

And the third is they have tactical or point knowledge management solutions that have not kept pace with the AI expectations that these businesses have. Those are the 3, I'd say, most popular ones.

Since there are no more questions, this concludes the question-and-answer session. I would like to turn the conference back over to management for any closing remarks. Please go ahead.

Well, thanks, everyone, for taking the time today. We look forward to providing you the update when we do our Q3 results.

Thanks.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.

Good day, and welcome to eGain Fiscal 2026 First Quarter Financial Results Call. [Operator Instructions] Please note this event is being recorded.

I would now like to turn the conference over to Jim Byers, PondelWilkinson, Investor Relations. Please go ahead.

Thank you, operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2026 First Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy; and Chief Financial Officer, Eric Smit.

Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements, which convey management's expectations, beliefs, plans, objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate or similar expressions. Forward-looking statements are protected by the safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects. Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, November 12, 2025, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call.

In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non-GAAP operating income. The tables included with the earnings press release include a reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures. eGain's earnings press release can be found by clicking the press release link on the Investor Relations page of eGain's website at egain.com. Along with the earnings release, we will also post an updated investor presentation to the Investor Relations page of eGain's website. And lastly, a phone replay of this conference call will be available for 1 week.

And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim, and good afternoon, everyone. We are off to a good start to our fiscal year with strong first quarter results across the board and ahead of consensus. Our total revenue was up 8% year-over-year. Our SaaS revenue was up 10% year-over-year. And our AI knowledge line of product business, the ARR for it was up 23% year-over-year. So we're making progress in the right direction in this exciting space of AI knowledge that we are now firmly leading.

Turning to business highlights. Let me share a couple of notable new logo wins in the quarter. First, we signed up one of New York's largest insurers -- health insurers. They serve 2 million members roughly. This company saw the critical gap in their business operation where lack of trusted knowledge that was available to all their employees and clinical staff was hurting compliance. They were struggling with their knowledge content fragmented across silos, some were SharePoint, some were Confluence and then many other repositories. So following an extensive process RFP and so on, they selected us to centralize their knowledge across the business for 8,000 users.

Now what's interesting to us is within 100 days, they've gone live with our solution with a powerful, scalable AI experience that aligns perfectly with their mission to deliver great health care to New Yorkers.

Another one, which is quite interesting is a multinational energy company that we recently won. They serve over 20 million customers across gas and electricity. This company, they have set up an ambitious goal to achieve a top quartile CSAT ranking because that would enable them to compete for millions of pounds of government incentives.

A key challenge for them as part of this is replacing their outdated knowledge experience for something that is spread across thousands of different documents, PowerPoints, SharePoint, word documents. And so they're replacing that with eGain, a modern solution to support their contact center, their field agents and their service engineers. Again, this client went live within 100 days. They're already live now with our AI knowledge solution. They internally call it WATT, WATT, a fitting name for a knowledge tool in an energy company.

On the expansion front, we are seeing a nice pattern where existing knowledge clients are increasingly coming to us to add the AI agent products to leverage the trusted knowledge they have in their knowledge base and either going to the contact center to assist the agents or in some cases, expand it into customer self-service directly. So for example, one of our branded manufacturing clients last quarter decided to expand their core knowledge implementation now into customer self-service.

And this is a leading manufacturer of premium bicycles. And what we have been able to now do is reduce a lot of their inbound contact center volume, and they can drive a lot more growth without increasing cost of service. So this combination of AI and knowledge is really working for our customers now, and we are seeing more and more of them starting to drive that expansion and build-out of agentic capabilities on top of a trusted knowledge infrastructure from us.

The other thing, just looking at the exciting stuff we've announced recently is we made a whole bunch of new product announcements at our Solve 25 event last month. It was very successful. We held it in Chicago. About half a dozen of our Global 2000 clients shared success stories with pretty much a common theme, and that was the power of trusted knowledge and agentic AI working hand-in-hand to deliver both contact center improvements in performance as well as self-service improvements.

The 3 new capabilities that we announced at the event, incidentally, that's our biggest product announcement ever where we have announced 3 new capabilities. So that's sort of testimonial to the great work that I've been -- our team has been doing for the last 1.5 years in this area of AI knowledge. So the first product we announced was more a process that we have now translated into a product. This was our -- what used to be called the eGain Knowledge method.

The knowledge method was our way, our proprietary way of building out and maintaining enterprise knowledge in a very efficient way for our business clients. And what we've been able to do is to apply AI to that and develop what we are now calling the eGain AI knowledge method. This includes comprehensive AI-powered knowledge intelligence, content orchestration and answer synthesis capabilities. All that put together with expert in the loop to ensure that quality and compliance are always front and center.

With these automation capabilities, we are confident that we can accelerate most knowledge management tasks by a factor of 10, and also reduce customer implementation time by 2x to 3x. In fact, when I mentioned the other 2 clients earlier, they were benefiting from some of the early usage of this capability of AI knowledge methods.

Our attendees at the conference were simply delighted with this because for the longest time, knowledge management has been a great ROI, but the investment required has been a lot of manual labor. Now with automating that with our AI capabilities, that just creates a lot of energy around knowledge and the priority businesses are able to put on to knowledge automation, not knowledge management.

The next product we announced was the second version of eGain AI Agent. And so we -- it's a breakthrough approach we have taken now to enhance the first version to automate conversations in customer service. And what we are doing there is we are combining hybrid AI with in band expert assurance where needed. And by doing that, we can deliver nontrivial use cases, which involve transactions, which involve decisions that are compliance oriented to customers using AI and automation.

And that's something which we have seen is a big gap in the industry where a lot of agentic solutions that are out there now talk about in automation, but really, when we see the automation in practice, it's all around very trivial use cases because these agentic AI solutions will occasionally make mistakes. And so you need to have a hybrid AI approach where you combine the deterministic reasoning with model reasoning and you do that intelligently and with compliance in mind. And that's what we have implemented in AI Agent to.

Finally, we announced the eGain Composer, which is our first product, first product targeting developers. It's a modular AI knowledge platform for developers that leverages trusted knowledge sources to power agentic automation. And so now what we're doing is these developers can now mix and match the components we have on our platform, including the Knowledge Hub, the retrieval engine, the agentic flows and the user experiences. So they can decide to configure what we already have, or they can decide to develop their own components and plug them in to meet specific needs using our extensive APIs and SDKs and connectors that we announced as part of the Composer solution.

Now over the last couple of years, we've been working with many enterprise AI teams in our clientele. And what we have seen is a common pattern where these AI teams are looking to deploy the same sort of reliable, trusted knowledge-powered agentic capabilities increasingly that we are doing in the customer service groups and CX groups. They want to do it outside of those groups. But in order to do that, they need more flexibility. They want to use the common knowledge architecture and infrastructure, but they also want the ability to develop their own Agentic layer on top of it. And the Composer offering allows them to do just that. So we've seen some good interest from our existing customers around Composer and some new partner interest in that in the short period of time that we have announced it. Very exciting for us.

Looking at the third aspect, which is, again, very interesting now for us is the team and how we are adding some key talent as we are growing the business and investing on top of this product-led growth plan that we have put in place. So a couple of new hires I want to highlight. We did a press release around John Copeland, who is our new VP of Marketing. He comes in from ServiceNow. And before that, he was with Adobe and eBay and McKinsey. He is leading our marketing effort. Our Solve event last month was his public introduction to the eGain stage.

The next person that I want to mention and very happy to have him on board is Vikas Paliwal. Vikas has joined as our VP of Product Marketing. He comes to us from the AWS AI team. And before that, he was with Intel and Broadcom and a host of other technology-heavy companies. He brings deep experience in the product development, the product management and now leading product marketing. So at eGain, his first mandate is to drive the GTM for our new Composer product. As I mentioned, we are targeting developers and partners who are looking for a composable platform to operationalize their AI solutions built on trusted knowledge.

And finally, we're very happy to have Gautam Garg, who's joined us as the VP of Finance. He's joined Eric's team and comes to us from BTIS, which is a respected investment bank and before that with Oracle in a variety of roles, starting in engineering and moving on to business and product. So Eric, I know is quite excited about having him on the team, looking to kind of further automate our finance and operations as we drive our growth plans.

So with a lot going on, just to conclude, we are pleased with our first quarter financial performance and what I see as growing market momentum. Our recent product introductions are resonating in the marketplace, the 3 we talked about. So that's the time we are now investing in building out the team. And so we are excited to expand our leadership team with the hires that I just mentioned.

With that, I'll hand it over to Eric, our CFO, to provide more detail on the financials. Eric?

Great. Thanks, Ashu, and thanks, everyone, for joining us today. Before I begin, I want to mention that we are again using slides to support our earnings call. We believe this will provide helpful context and make it easier for you to follow our results and outlook. In addition to the webcast, you can find the slides in the Investor Relations section of our website and the updated investor presentation.

As Ashu noted, we are off to a good start to our fiscal year with revenue and ARR growth year-over-year, expanded margins, increased profitability and strong cash flow from operations. Let me share more details about our financial results for Q1 before discussing our outlook and guidance for Q2 of fiscal 2026.

Looking at our revenue, total revenue for the first quarter was $23.5 million, reaching the high end of our guidance and increasing 8% year-over-year.

SaaS revenue increased by 10% year-over-year and accounted for 93% of total revenue versus 91% of total revenue in Q1 of '25.

Looking at non-GAAP gross profits and gross margins. Total gross margin for the quarter was 76%, up 600 basis points from 70% a year ago. SaaS gross margin for the quarter was 81%, up from 77% a year ago. Total gross margin expansion was driven by SaaS gross margin expansion plus a greater shift towards SaaS versus professional services revenue.

Notably, SaaS gross margin expansion was primarily driven by our product enhancements that enabled more cost-efficient deployments and delivered operational efficiencies within our cloud and customer support teams.

Now turning to our operations. Non-GAAP operating costs for the first quarter were $18.8 million, down 9% year-over-year as we have streamlined and realigned our business operations because of automation and the continued shift towards a product-led sales model. Notably, we have redeployed these cost savings into R&D, which reflects our ongoing focus on growth and product innovation.

Looking at our bottom-line, our GAAP net income includes a $1.4 million warrant expense recorded as a onetime stock-based charge during the quarter. As we mentioned on our last call, this expense relates to the warrant we issued to JPMorgan in August as part of the strategic agreement that includes the appointment of a senior JPMC executive as a Board observer.

Non-GAAP net income was $4.7 million or $0.17 per share up significantly from non-GAAP net income of $1.3 million or $0.04 per share in the year ago quarter. Adjusted EBITDA margin for the quarter was 21%, exceeding our guidance and up from 6% in the year ago quarter.

Turning to our balance sheet and cash flows. For the first quarter, we generated strong cash flow from operations of $10.4 million or 44% operating cash flow margin compared to $1 million or 4% operating cash flow margin in the year ago quarter. This was ahead of our internal target due to better-than-expected cash collection efforts in the quarter.

During the quarter, we bought back $1.5 million in stock at an average price of $6.38 per share.

Our balance sheet remains very strong. Total cash and cash equivalents at the end of the quarter was $70.9 million, up from $62.9 million as of June 30, 2025.

Now turning to our customer metrics. I've broken out the ARR AI Knowledge metrics from the total metrics to highlight the momentum in our AI Knowledge business. Looking at ARR, SaaS ARR for Knowledge customers increased 23% year-over-year, while SaaS ARR for all customers increased 8% year-over-year.

Turning to our net retention rates. LTM dollar-based SaaS net retention for Knowledge customers was 112%, up from 103% a year ago, while net retention for all customers was 102%, up from 90% a year ago. Our LTM dollar-based SaaS net expansion rate was 119% for our Knowledge customers and 110% for all our other customers, all customers.

Looking at remaining performance obligations. Total RPO increased 23% year-over-year, and our short-term RPO of $58 million was up 7% year-over-year.

Now turning to our guidance. For the second quarter of fiscal 2026, we expect total revenue of between $22.3 million to $22.8 million. The sequential decline is primarily due to an approximate $600,000 reduction in revenue from our messaging platform business, which, as we had mentioned on our last call, we will be sunsetting over the next year. Furthermore, the recent government shutdown has introduced delays and near-term uncertainty for certain professional services engagements with some of our government customers.

Turning to the bottom-line. For Q2, we expect GAAP net income of $1.2 million to $1.7 million or $0.04 to $0.06 per share, which includes stock-based compensation expense of approximately $700,000.

We expect non-GAAP net income of $1.9 million to $2.4 million or $0.07 to $0.08 per share and adjusted EBITDA of $2.7 million to $3.2 million or a margin of 12% to 14%.

Looking at our full fiscal year ended June 30, 2026, we expect total revenue to be between $90.5 million and $92 million, representing a return to growth for the year.

GAAP net income of $3.5 million to $5 million or $0.12 to $0.17 per share. This includes stock-based comp expense of approximately $3.4 million and the warrant expense of approximately $1.4 million.

Non-GAAP net income of $8.3 million to $9.8 million or $0.29 to $0.34 per share and adjusted EBITDA of $10.4 million to $11.9 million or a margin of 11% to 13%.

Looking at our diluted weighted average shares outstanding with the recent stock price movement, we now expect approximately 28.8 million shares for both the second quarter and full year. This expected increase in outstanding shares has a 1% impact to our EPS guidance for FY '26.

In conclusion, we are off to a good start to the year with solid results that beat consensus across the board. Customers and partners are responding enthusiastically to our expanded suite of AI-enabling knowledge solutions, and we are well positioned to build on this momentum and drive sustainable growth and profitability going forward.

Finally, on the IR front, eGain will be meeting with investors at the 14th Annual ROTH Technology Conference in New York City on November 19. We look forward to seeing some of you there in person.

With that, I'd like to open the call for questions. Operator?

[Operator Instructions] And your first question today will come from Richard Baldry with ROTH Capital.

It looks like sales trends are sort of firming up and -- but by contrast, the actual dollar spent on sales and marketing in the quarter stepped down sort of sequentially and year-over-year. Can you talk about what your strategy is there, whether you think that it might be time to start investing there more aggressively? Or do you feel that you have the capacity you need to address the opportunities that are starting to come?

Yes. So 2 things. One, there's just a summer slowdown with marketing spend. So for example, for this current quarter, again, things will go up, right, because the marketing spend is going up.

Second, to your point about sort of building out capacity, I think right now, people-wise, we have good capacity because we are driving, as we mentioned, sort of a product-led sales motion with much more solution expertise-oriented pipeline execution. And so that is working well for us. But we do think that in the second half of this fiscal year, we will step up the sales hiring investment as well.

And it looks like agentic AI Agents starting to highly proliferate them, be generous. But none of them -- because it seems like integrating a Gen AI engine with some of that functionality is okay, easy enough, but they don't seem to have access to the data behind it. So do you think that the proliferation of those, what I think of as almost dumb agents actually helps you or does it commoditize that segment enough that it will push more people to try to have to work with partners like yourself to make them specifically intelligent on a customer-by-customer basis?

I believe so. I believe so. I think that's what we are seeing. We are seeing people playing with AI, agentic AI, and then we are seeing them talking to us after that, right? And so yes, to your point, if you want to do something serious, you need to make sure that you're not feeding a garbage that you're connecting into really critical data systems to solve nontrivial experiences and use cases. And that's where we are coming in. And we are seeing that even with our existing customers. They have -- they are large companies. They have lots of AI teams that are developing cute little prototypes on the side, but they all are coming back to us to say, okay, we need to connect to your knowledge back end. So it's going to be exciting for us.

And last for me, if you look at the Composer product, it's a new set of targets you're going after with developers. How complete is that for them to pull in to be able to work with not only the different components that you can bring to the table, but can they integrate with a variety of GenAI engines? Or is it specific to a few you've tuned it for? How difficult would it be to sort of pop in different AI engines on top of your system?

Yes. Great question. So to your point, this is the first version. So by definition, it's not the most comprehensive. But what we have going for us, I believe, is 2 things.

One, we have the trusted knowledge back end and making that available very easily for people to plug into whatever agentic solution they are building. And so that we have made it very easy with our APIs and SDKs. So for example, we will plug into OpenAI's development environment or Azure, Copilot development environment. So if people are building agentic workflows in other places, we are providing a connector into those work environments so that they can use those -- use our APIs to get the trusted knowledge. So that is point one.

The second is we are also -- to your earlier -- second part of your question, we are enabling it within what we call the bring your own model architecture. So if an enterprise says, no, we are going to use Claude and not use OpenAI, that's fine. We are making it possible for people to plug in their choice model into our platform to do AI stuff. So those are the 2 things that we have done to make it easy for people to start consuming right away.

Great. I thought that was my last question, but I'll try to sneak in one more. You've got a pretty big cash balance now and it went up pretty substantially in the quarter despite some buybacks. Can you talk about strategically what you really think that's best deployed against? Is it a more aggressive buyback? Is it tuck-in acquisitions to expand your capabilities in this knowledge management, agentic world? So how do we think about that asset?

Eric, do you want to talk to that?

Sure. I mean I think we'll obviously continue to look at the buyback. I mean, as we've disclosed in the last call, we had increased that capacity. So that certainly still continues to be one avenue. But I think to your point of sort of exploring tuck-in acquisitions or inorganic, it's something that we're always open to, but for sure, it is not our primary focus. But I think more importantly, just having that ability to continue to invest in the business as well is something that gives us that comfort with that buffer. So we don't have any distraction around the viability of the business. So those are the items that I would highlight.

Congrats on a good quarter.

Thank you.

Next question will come from Jeff Van Rhee with Craig-Hallum.

This is Vijay Homan on for Jeff Van Rhee. First for me, just last quarter, you talked about a pipeline of several 7-figure knowledge Hub opportunities and kind of an increasing pilot conversion rate. I was wondering if you could just give us an update on those opportunities and what you're seeing in those sales cycles.

Yes. So I think the progress is good. Progress is -- us -- I'd say these large opportunities take time to mature. And so what we are seeing is steady progress. So I'm quite pleased with that because with large opportunities, sometimes they stall along the way. I'm not seeing that. I'm seeing steady progression. So I'm quite excited. And I also see more engagement on the partner side. We are seeing more interest from partners who are starting to see this area as a viable category to add value. So those are 2 things that are quite exciting for us. First, the pipeline progression; and second, starting to see some good partner activity.

Got it. Appreciate that. And then next one, I know the intent was for the -- for JPMorgan to deploy by the late fall. I was just wondering how that deployment is going? And any other updates on that rollout or learnings would be helpful.

Yes. So yes, I'm happy to report that they have gone live with what the first phase plan was, and we are actively working the next phase. So I think it's gone to plan, which is great. As I think I've mentioned earlier, we deployed their first phase in half the time that we had originally discussed and agreed with them on. So it's pretty exciting for us. The speed of getting these projects live and used is, to me, a barometer of the interest and priority that enterprises start to place on these kinds of technologies.

[Operator Instructions] We see no further questions. This will conclude our question-and-answer session. I would like to turn the conference back over to management for any closing remarks.

Thanks, operator, and thanks, everybody, for participating and look forward to updating you with our Q2 results. Thank you.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.